Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "202" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 34 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 34 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460017 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.563349 | 0.921430 | 1.198406 | -1.148349 | 0.388934 | -0.524280 | -0.710032 | 35.733919 | 0.5713 | 0.5630 | 0.3390 | nan | nan |
| 2460016 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.573757 | 0.885028 | 1.130202 | -1.337504 | 0.348191 | -0.006004 | -1.252890 | 50.735947 | 0.5787 | 0.5722 | 0.3423 | nan | nan |
| 2460015 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.704381 | 0.862812 | 1.213148 | -1.322030 | 0.710248 | -0.253058 | -1.421455 | 48.471310 | 0.5884 | 0.5801 | 0.3407 | nan | nan |
| 2460014 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.383107 | 1.416985 | 0.966202 | -1.207021 | -0.029326 | 0.325884 | -0.831401 | 49.659528 | 0.5635 | 0.5526 | 0.3448 | nan | nan |
| 2460013 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.573961 | 0.855730 | 1.231481 | -1.268718 | 0.757013 | 0.297695 | -1.507303 | 59.735152 | 0.5817 | 0.5809 | 0.3468 | nan | nan |
| 2460012 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.385900 | 0.654862 | 1.069172 | -1.382528 | 0.715915 | 0.312332 | -1.776351 | 71.872089 | 0.5834 | 0.5840 | 0.3403 | nan | nan |
| 2460011 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.650522 | 0.552024 | 1.654019 | -1.667344 | 2.431098 | 0.728950 | -1.011438 | 60.822202 | 0.6091 | 0.6085 | 0.3358 | nan | nan |
| 2460010 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.943931 | 0.915634 | 1.865845 | -1.354845 | 1.355928 | -0.189916 | -1.489862 | 45.409473 | 0.6217 | 0.6247 | 0.3390 | nan | nan |
| 2460009 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.662753 | 0.204979 | 1.610500 | -1.481321 | 1.241520 | 0.104786 | -1.791951 | 47.038473 | 0.6232 | 0.6288 | 0.3445 | nan | nan |
| 2460008 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.905402 | 0.797408 | 1.903557 | -1.640638 | 1.032538 | -0.373939 | 0.505734 | 12.568642 | 0.6573 | 0.6678 | 0.3125 | nan | nan |
| 2460007 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.538893 | 2.784483 | 1.353361 | -1.343219 | 0.484591 | -0.234650 | -1.470049 | 24.629612 | 0.6276 | 0.6235 | 0.3261 | nan | nan |
| 2459999 | digital_ok | 0.00% | 0.08% | 0.17% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5652 | 0.5570 | 0.3255 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.705331 | 2.321304 | 1.269390 | -1.148868 | 0.169845 | 0.276522 | -1.381878 | 30.214959 | 0.6061 | 0.6004 | 0.3680 | nan | nan |
| 2459997 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.800932 | 2.485666 | 1.535402 | -1.091795 | 0.911291 | -0.350981 | -1.934731 | 41.444822 | 0.6145 | 0.6110 | 0.3733 | nan | nan |
| 2459996 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.893802 | 2.415787 | 1.457181 | -1.112706 | 1.089801 | -0.815684 | -0.932053 | 22.876606 | 0.6282 | 0.6234 | 0.3806 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.883978 | 2.516088 | 1.582207 | -1.282168 | 1.079400 | 0.606583 | -0.979885 | 23.938983 | 0.6203 | 0.6161 | 0.3712 | nan | nan |
| 2459994 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.686631 | 3.347646 | 1.517296 | -1.276174 | 1.047824 | -0.504910 | -0.536657 | 21.352535 | 0.6150 | 0.6080 | 0.3679 | nan | nan |
| 2459993 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.075853 | 3.913719 | 1.778259 | -1.179625 | 1.529713 | -0.147077 | -0.574078 | 19.250037 | 0.5950 | 0.5998 | 0.3841 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.997024 | 4.073281 | 1.736309 | -1.235881 | 1.241864 | -0.496051 | -0.337793 | 21.123720 | 0.6223 | 0.6037 | 0.3799 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.838249 | 3.762626 | 1.742601 | -1.198151 | 1.004005 | -0.510469 | -0.560318 | 22.314874 | 0.6205 | 0.6034 | 0.3763 | nan | nan |
| 2459989 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.691800 | 3.892376 | 1.765088 | -1.017243 | 0.640352 | -0.435682 | -0.236376 | 16.121469 | 0.6110 | 0.6007 | 0.3758 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.949201 | 4.451831 | 1.765235 | -1.219987 | 0.909288 | -0.644057 | -0.365404 | 17.902370 | 0.6163 | 0.6071 | 0.3677 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.475552 | 3.454579 | 1.523143 | -1.289557 | 0.287472 | -0.341958 | -0.655326 | 34.793735 | 0.6233 | 0.6116 | 0.3652 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.966976 | 4.132432 | 1.687391 | -1.295110 | 1.028421 | -0.688826 | 0.633045 | 13.163940 | 0.6391 | 0.6358 | 0.3320 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.599900 | 3.822533 | 1.407868 | -1.298148 | 0.693483 | -0.793299 | -0.769955 | 39.653522 | 0.6244 | 0.6124 | 0.3741 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.392458 | 3.199609 | 1.447020 | -1.155738 | 1.124725 | -0.526351 | -0.686512 | 21.125962 | 0.6390 | 0.6326 | 0.3498 | nan | nan |
| 2459983 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.626248 | 2.232759 | 1.591622 | -1.202895 | 1.090914 | -0.560431 | -0.286083 | 19.509643 | 0.6468 | 0.6545 | 0.3155 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.402392 | 1.737633 | 0.870737 | -1.103858 | -0.797831 | -0.417027 | -0.373859 | 1.252640 | 0.6913 | 0.6822 | 0.2885 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.637740 | 2.969834 | 1.917545 | -1.223252 | 1.538911 | 0.439324 | -0.625353 | 25.482117 | 0.6243 | 0.6143 | 0.3713 | nan | nan |
| 2459980 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.252883 | 3.062687 | 1.371384 | -1.084004 | 0.555619 | -1.023715 | 0.709655 | 4.766080 | 0.6605 | 0.6489 | 0.3070 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.560632 | 3.450426 | 1.475943 | -1.042069 | 0.657754 | -0.548040 | -0.814350 | 18.965356 | 0.6161 | 0.6005 | 0.3742 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.600496 | 3.760381 | 1.720459 | -1.214272 | 1.009146 | -0.635011 | -0.005985 | 25.562019 | 0.6162 | 0.6000 | 0.3802 | nan | nan |
| 2459977 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.785450 | 3.112110 | 1.415305 | -1.091165 | 1.367553 | -0.838464 | -1.325849 | 28.791282 | 0.5815 | 0.5683 | 0.3383 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.728509 | 3.345002 | 1.590641 | -1.240456 | 1.059901 | -0.185631 | -0.495008 | 20.406174 | 0.6235 | 0.6076 | 0.3703 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 35.733919 | 0.921430 | 0.563349 | -1.148349 | 1.198406 | -0.524280 | 0.388934 | 35.733919 | -0.710032 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 50.735947 | 0.885028 | 0.573757 | -1.337504 | 1.130202 | -0.006004 | 0.348191 | 50.735947 | -1.252890 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 48.471310 | 0.862812 | 0.704381 | -1.322030 | 1.213148 | -0.253058 | 0.710248 | 48.471310 | -1.421455 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 49.659528 | 0.383107 | 1.416985 | 0.966202 | -1.207021 | -0.029326 | 0.325884 | -0.831401 | 49.659528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 59.735152 | 0.573961 | 0.855730 | 1.231481 | -1.268718 | 0.757013 | 0.297695 | -1.507303 | 59.735152 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 71.872089 | 0.385900 | 0.654862 | 1.069172 | -1.382528 | 0.715915 | 0.312332 | -1.776351 | 71.872089 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 60.822202 | 0.650522 | 0.552024 | 1.654019 | -1.667344 | 2.431098 | 0.728950 | -1.011438 | 60.822202 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 45.409473 | 0.943931 | 0.915634 | 1.865845 | -1.354845 | 1.355928 | -0.189916 | -1.489862 | 45.409473 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 47.038473 | 0.662753 | 0.204979 | 1.610500 | -1.481321 | 1.241520 | 0.104786 | -1.791951 | 47.038473 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 12.568642 | 0.797408 | 0.905402 | -1.640638 | 1.903557 | -0.373939 | 1.032538 | 12.568642 | 0.505734 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 24.629612 | 0.538893 | 2.784483 | 1.353361 | -1.343219 | 0.484591 | -0.234650 | -1.470049 | 24.629612 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 30.214959 | 0.705331 | 2.321304 | 1.269390 | -1.148868 | 0.169845 | 0.276522 | -1.381878 | 30.214959 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 41.444822 | 0.800932 | 2.485666 | 1.535402 | -1.091795 | 0.911291 | -0.350981 | -1.934731 | 41.444822 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 22.876606 | 0.893802 | 2.415787 | 1.457181 | -1.112706 | 1.089801 | -0.815684 | -0.932053 | 22.876606 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 23.938983 | 0.883978 | 2.516088 | 1.582207 | -1.282168 | 1.079400 | 0.606583 | -0.979885 | 23.938983 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 21.352535 | 0.686631 | 3.347646 | 1.517296 | -1.276174 | 1.047824 | -0.504910 | -0.536657 | 21.352535 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 19.250037 | 1.075853 | 3.913719 | 1.778259 | -1.179625 | 1.529713 | -0.147077 | -0.574078 | 19.250037 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 21.123720 | 0.997024 | 4.073281 | 1.736309 | -1.235881 | 1.241864 | -0.496051 | -0.337793 | 21.123720 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 22.314874 | 3.762626 | 0.838249 | -1.198151 | 1.742601 | -0.510469 | 1.004005 | 22.314874 | -0.560318 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 16.121469 | 3.892376 | 0.691800 | -1.017243 | 1.765088 | -0.435682 | 0.640352 | 16.121469 | -0.236376 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 17.902370 | 4.451831 | 0.949201 | -1.219987 | 1.765235 | -0.644057 | 0.909288 | 17.902370 | -0.365404 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 34.793735 | 0.475552 | 3.454579 | 1.523143 | -1.289557 | 0.287472 | -0.341958 | -0.655326 | 34.793735 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 13.163940 | 4.132432 | 0.966976 | -1.295110 | 1.687391 | -0.688826 | 1.028421 | 13.163940 | 0.633045 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 39.653522 | 3.822533 | 0.599900 | -1.298148 | 1.407868 | -0.793299 | 0.693483 | 39.653522 | -0.769955 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 21.125962 | 0.392458 | 3.199609 | 1.447020 | -1.155738 | 1.124725 | -0.526351 | -0.686512 | 21.125962 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 19.509643 | 0.626248 | 2.232759 | 1.591622 | -1.202895 | 1.090914 | -0.560431 | -0.286083 | 19.509643 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Shape | 1.737633 | -0.402392 | 1.737633 | 0.870737 | -1.103858 | -0.797831 | -0.417027 | -0.373859 | 1.252640 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 25.482117 | 2.969834 | 0.637740 | -1.223252 | 1.917545 | 0.439324 | 1.538911 | 25.482117 | -0.625353 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 4.766080 | 3.062687 | 0.252883 | -1.084004 | 1.371384 | -1.023715 | 0.555619 | 4.766080 | 0.709655 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 18.965356 | 0.560632 | 3.450426 | 1.475943 | -1.042069 | 0.657754 | -0.548040 | -0.814350 | 18.965356 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 25.562019 | 3.760381 | 0.600496 | -1.214272 | 1.720459 | -0.635011 | 1.009146 | 25.562019 | -0.005985 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 28.791282 | 0.785450 | 3.112110 | 1.415305 | -1.091165 | 1.367553 | -0.838464 | -1.325849 | 28.791282 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 202 | N18 | digital_ok | nn Temporal Discontinuties | 20.406174 | 3.345002 | 0.728509 | -1.240456 | 1.590641 | -0.185631 | 1.059901 | 20.406174 | -0.495008 |